code: def predict(
      self, sentences: Union[List[Sentence], Sentence], *args, **kwargs
  ):
      metadata = kwargs.pop("metadata", None)
      result = self.__model__.predict(sentences, *args, **kwargs)
  
      if not isinstance(sentences, list):
          sentences = [sentences]
  
      if not metadata:
          metadata = [{}] * len(sentences)
  
      filtered_data = [
          (sentence, meta)
          for sentence, meta in zip(sentences, metadata)
          if self.is_record_accepted()
      ]
      if filtered_data:
          self._log_future = self.send_records(filtered_data)
  
      return result
  
file_path: argilla/src/argilla/monitoring/_flair.py
imports: from datetime import datetime
  from typing import Any, Dict, List, Optional, Tuple, Union
  from argilla import TokenClassificationRecord
  from argilla.client.api import Api
  from argilla.monitoring.base import BaseMonitor
  from argilla.monitoring.types import MissingType
label_desc: Use image node_id 2 for calling the FlairMonitor obj's underlying member method code with example usage: obj.predict(sentences) and returns: result